
#Australia
AUS.gender <- data.frame(gender=c("Male","Female"), 
                         Freq=c(7521290,7782796)) #7521290,7782796

AUS.region <- data.frame(REGION_0=c("West+North","Queen+South","NSW+AUCapT","Vict+Tasm"), 
                         Freq=c(1725713,4173984,5147800,4256589))

AUS.education <- data.frame(education=c("Low","Medium","High"), 
                            Freq=c(1244397,9932468,4127221))

AUS.age <- data.frame(age=c("18-29","30-39","40-49","50-59","60-99"), 
                      Freq=c(3333630,2853372,2737845,2532030,3847209))

  
#Brazil

BRA.gender <- data.frame(gender=c("Male","Female"), 
                         Freq=c(124326,137308))

BRA.region <- data.frame(REGION_0=c("North","North East","Central","South","South East"), 
                         Freq=c(38770,73606,27264,41635 ,80359))

BRA.education <- data.frame(education=c("Low","Medium","High"), 
                            Freq=c(120352,99421,41861))

BRA.age <- data.frame(age=c("18-29","30-39","40-49","50-59","60-99"), 
                      Freq=c(66560,56345,48710,40799,49220))


#Canada

CAN.gender <- data.frame(gender=c("Male","Female"), 
                         Freq=c(350704,369206))

CAN.region <- data.frame(REGION_0=c("Atlantic Canada","British Columbia","Prairies","Ontario","Quebec"), 
                         Freq=c(48093,97765,125591,278402,170059))

CAN.education <- data.frame(education=c("Low","Medium","High"), 
                            Freq=c(109875,436644,173391))

CAN.age <- data.frame(age=c("18-29","30-39","40-49","50-59","60-99"), 
                      Freq=c(143982,122385,115186,122384,215973))

#Chile

CHI.gender <- data.frame(gender=c("Male","Female"), 
                         Freq=c(6328350 ,6793892))

CHI.region <- data.frame(REGION_0=c("Center","North","South"), 
                         Freq=c(9356164,1600438,2165640 ))

CHI.education <- data.frame(education=c("Low","Medium","High"), 
                            Freq=c(3219944,5915567,3986731))

CHI.age <- data.frame(age=c("18-25","26-35","36-45","46-55","56+"), 
                      Freq=c(2150449,2700854,2351299,2301758,3617882))


#China


CHN.gender <- data.frame(gender=c("Male","Female"), 
                         Freq=c(500000,500000))


CHN.region <- data.frame(REGION_0=c("North","East","South Central","West"), 
                         Freq=c(140000,410000,240000,210000))

CHN.education <- data.frame(education=c("Low","Medium","High"), 
                            Freq=c(760000,140000,100000))

CHN.age <- data.frame(age=c("18-29","30-39","40-49","50-59","60-99"), 
                      Freq=c(160000,150000,180000,190000,320000))

#Colombia


COL.gender <- data.frame(gender=c("Male","Female"), 
                         Freq=c(23348500,24301500))

COL.region <- data.frame(REGION_0=c("Andina","Caribe","Pacifico","Orinaquia","Amazonia"), 
                         Freq=c(21442500,15248000,7624000,1429500,1906000))

COL.education <- data.frame(education=c("Low","Medium","High"), 
                            Freq=c(21919000,15248000,10483000))

COL.age <- data.frame(age=c("18-29","30-39","40-49","50-59","60-99"), 
                      Freq=c(12865500,9530000,7624000,7624000,10006500))

#France

FRA.gender <- data.frame(gender=c("Male","Female"), 
                         Freq=c(7072918,7846937))

FRA.region <- data.frame(REGION_0=c("Ile de France","North West","North East","South West","South East"), 
                         Freq=c(3367093,3998384,1743361,1263726,4547291))

FRA.education <- data.frame(education=c("Low","Medium","High"), 
                            Freq=c(2203542,5093229,7623084 ))

FRA.age <- data.frame(age=c("18-29","30-39","40-49","50-59","60-99"), 
                      Freq=c(2910325,2460969,2435571,2367969,4745021))


#India

IND.gender <- data.frame(gender=c("Male","Female"), 
                         Freq=c(387259672,372783087))

IND.region <- data.frame(REGION_0=c("North","East+NorthEast","South","Central+West"), 
                         Freq=c(178088190,191576829,174816038,215561702))

IND.education <- data.frame(education=c("Low","Medium","High"), 
                            Freq=c(525613234,158134873,74070418))

IND.age <- data.frame(age=c("18-29","30-39","40-49","50-59","60-99"), 
                      Freq=c(260991134,173275377,134370834,87950243,103455171))

#Italy

ITA.gender <- data.frame(gender=c("Male","Female"), 
                         Freq=c(29045232,31314768))

ITA.region <- data.frame(REGION_0=c("Center","North West","North East","South+Islands"), 
                         Freq=c(9054000,12674600,16900800,21729600))

ITA.education <- data.frame(education=c("Low","Medium","High"), 
                            Freq=c(23087700,25604712,11661552))

ITA.age <- data.frame(age=c("18-29","30-39","40-49","50-59","60-99"), 
                      Freq=c(8824632,8589228,11232996,10973448,20739696))

#Spain


SPA.gender <- data.frame(gender=c("Male","Female"), 
                         Freq=c(22799700,24200300))

SPA.region <- data.frame(REGION_0=c("North","Center","East_Islands","South"), 
                         Freq=c(10058000,10998000,15980000,9964000))

SPA.education <- data.frame(education=c("Low","Medium","High"), 
                            Freq=c(4309900,20393300,22292100))

SPA.age <- data.frame(age=c("18-29","30-39","40-49","50-59","60-99"), 
                      Freq=c(6951300,7891300,9498700,8342500,14311500))


#Uganda


UGA.gender <- data.frame(gender=c("Male","Female"), 
                         Freq=c(12636000,13364000))

UGA.region <- data.frame(REGION_0=c("Northern Region","Eastern Region","Western Region","Central Region"), 
                         Freq=c(5200000,6760000,6760000,7280000))

UGA.education <- data.frame(education=c("Low","Medium","High"), 
                            Freq=c(19500000,5980000,520000))

UGA.age <- data.frame(age=c("18-29","30-39","40-49","50-59","60-99"), 
                      Freq=c(11440000,5980000,3900000,2340000,2340000))

#UK


UK.gender <- data.frame(gender=c("Male","Female"), 
                         Freq=c(32052160,33547840))

UK.region <- data.frame(REGION_0=c("NorthENG+Scot+NorthI","SouthENG+Wales","London+Midland+East"), 
                         Freq=c(22586080,17751360,25262560))

UK.education <- data.frame(education=c("Low","Medium","High"), 
                            Freq=c(9184000,35424000,20992000))

UK.age <- data.frame(age=c("18-29","30-39","40-49","50-59","60-99"), 
                      Freq=c(12726400,10758400,10627200,11152000,20336000))




#USA
US.gender <- data.frame(gender=c("Male","Female"), 
                        Freq=c(103950000,106050000))

US.region <- data.frame(REGION_0=c("Midwest","West","Northeast","South"), 
                        Freq=c(48300000,46200000,39900000,75600000))

US.education <- data.frame(education=c("Low","Medium","High"), 
                           Freq=c(19257000,91119000,99603000))

US.age <- data.frame(age=c("18-29","30-39","40-49","50-59","60-99"), 
                     Freq=c(51030000,39270000,42630000,41370000,35700000))

